March 29, 2024, 4:42 a.m. | Cynthia Maldonado-Garcia, Rodrigo Bonazzola, Enzo Ferrante, Thomas H Julian, Panagiotis I Sergouniotis, Nishant Ravikumara, Alejandro F Frangi

cs.LG updates on

arXiv:2403.18873v1 Announce Type: cross
Abstract: We investigated the potential of optical coherence tomography (OCT) as an additional imaging technique to predict future cardiovascular disease (CVD). We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images and to capture distinct characteristics of different retinal layers within the OCT image. A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate …

abstract arxiv autoencoders cs.lg deep learning disease eess.iv future images imaging learn low optical risk type vae variational autoencoders

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Data Analyst (Commercial Excellence)

@ Allegro | Poznan, Warsaw, Poland

Senior Machine Learning Engineer

@ Motive | Pakistan - Remote

Summernaut Customer Facing Data Engineer

@ Celonis | Raleigh, US, North Carolina

Data Engineer Mumbai

@ Nielsen | Mumbai, India